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Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization

Author

Listed:
  • Pei Zhang

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

  • Xianpan Wu

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

  • Changqing Du

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

  • Hongming Xu

    (Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Huawu Wang

    (Dongfeng Commercial Vehicle Technical Center of DFCV, Wuhan 430056, China)

Abstract

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

Suggested Citation

  • Pei Zhang & Xianpan Wu & Changqing Du & Hongming Xu & Huawu Wang, 2020. "Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5407-:d:429107
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    References listed on IDEAS

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    1. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2019. "Optimal sizing and adaptive energy management of a novel four-wheel-drive hybrid powertrain," Energy, Elsevier, vol. 187(C).
    2. Yang, Ye & Zhang, Youtong & Tian, Jingyi & Li, Tao, 2020. "Adaptive real-time optimal energy management strategy for extender range electric vehicle," Energy, Elsevier, vol. 197(C).
    3. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    4. Lei, Zhenzhen & Qin, Datong & Hou, Liliang & Peng, Jingyu & Liu, Yonggang & Chen, Zheng, 2020. "An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information," Energy, Elsevier, vol. 190(C).
    5. Xixue Liu & Datong Qin & Shaoqian Wang, 2019. "Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor," Energies, MDPI, vol. 12(11), pages 1-17, May.
    6. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    7. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
    8. Shuxian Li & Minghui Hu & Changchao Gong & Sen Zhan & Datong Qin, 2018. "Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means," Energies, MDPI, vol. 11(6), pages 1-16, June.
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    Cited by:

    1. Muataz Abotabik & Richard T. Meyer, 2021. "Switched Optimal Control of a Heavy-Duty Hybrid Vehicle," Energies, MDPI, vol. 14(20), pages 1-20, October.
    2. Sven Schulze & Günter Feyerl & Stefan Pischinger, 2023. "Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions," Energies, MDPI, vol. 16(13), pages 1-29, July.

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